Module 01: Descriptive Statistics for Beginner Level
03 Interactive Classes 06 Hour Live Lecture 01 Industry-Standard Project Lesson 1: Introduction to Vectors and Matrices in Linear Algebra • Topics Covered: Introduction to Data Science course, course modules, introduction to vector and metrices, addition, subtraction, multiplication, inverse matrix, identity matrix • Objective: You will learn the fundamental concepts of vectors and matrices, including their operations, and how these mathematical tools are applied to solve complex problems in data science and engineering. Lesson 2: Foundations of Statistics for Data Science • Topics Covered: Statistics in Data Science, Data types and variables, Data organization, Tabular methods, Visualization methods • Objective: You will learn the fundamental concepts of statistics, focusing on data types, variables, and methods of organizing and presenting data through both tabular and visual approaches, enabling you to draw meaningful insights from data. Lesson 3: Descriptive Statistics and Data Distribution • Topics Covered: Measures of central tendency, mean, median, mode, measures of dispersion, variance, standard deviation, shape distribution, skewness, kurtosis • Objective: You will learn about measures of central tendency and dispersion, as well as how to analyze the shape of data distributions through skewness and kurtosis to understand the spread and symmetry of data
Module 02: Probability and Distribution for Data Analysis
02 Interactive Classes 04 Hours Live Lecture Lesson 1: Sampling Methods and Probability Fundamentals Topics Covered: Types of Sampling Methods (Simple Random, Stratified, Cluster, Systematic), Sampling Techniques and Strategies, Introduction to Probability, Probability Rules and Theorems, Random variables Objective: You will learn about various sampling methods, techniques, and strategies, and gain a comprehensive understanding of probability fundamentals, including rules, theorems, and random variables, to support accurate data analysis and decision-making. Lesson 2: Probability Distributions and the Central Limit Theorem Topics Covered: Types of distribution, discrete and continuous probability distribution, Binomial, Poisson, Uniform and Normal distribution, Central limit theorem Objective: You will learn about different types of probability distributions, both discrete and continuous, and gain an understanding of the Central Limit Theorem, which lays the foundation for understanding data behavior and statistical inference.
Module 03: Exploratory Data Analysis
02 Interactive Classes 04 Hours Live Lecture 01 Industry-Standard Project Lesson 1: Data Visualization Techniques and Representation Methods Topics Covered: Understanding data visualization, Data visualization techniques, line, scatter, bar, histogram, pie, stack, tools and Data representation methods Objective: You will learn data visualization techniques, including chart types like line, scatter, bar, and pie, as well as tools and best practices for effectively representing data and enhancing insight communication. Lesson 2: Exploratory Data Analysis and Visualization with Python Topics Covered: Understanding Exploratory Data Analysis, matplotlib, plotly and seaborn libraries, plotting criteria and methods, basic data analysis and visualization methods Objective: You will learn Exploratory Data Analysis (EDA) techniques using Python libraries like Matplotlib, Plotly, and Seaborn, focusing on plotting methods and data visualization to uncover patterns and insights.
Module 04: Regression Analysis and Applications
04 Classes 06 Hours Live Lectures 01 Industry Standard Project Class 1: Correlation and Regression Analysis Topics Covered: Types of Correlation, Correlation Coefficient, Properties of Correlation Coefficients, visualization and interpretation, Correlation Matrix and Heatmaps, types of Regression Analysis, simple linear regression, Assumptions of linear regression Objective: You will learn the concepts of correlation and regression, including types of correlation, correlation coefficients, and simple linear regression for modeling and predicting data trends. Class 2: Advanced Regression Diagnostics and Model Evaluation Topics Covered: Linearity, Multicollinearity, Homoscedasticity, performance matrix, R-square, MAD, MAPE, MSE, RMSE, Residual analysis, influential factors, cross validation, bias and variance, overfitting and underfitting Objective: You will learn advanced regression diagnostics and evaluation techniques, including assessing linearity, multicollinearity, and homoscedasticity, as well as performance metrics, residual analysis, cross-validation, and how to address overfitting and underfitting in regression models Class 3: Capstone Project 01 Topics Covered: Predictive Modeling of an industry dataset. Objective: Performing exploratory data analysis, handle multicollinearity, evaluate model performance using metrics like RMSE, and address overfitting and underfitting.
Module 05: Inferential Statistics for Data-Driven Analysis
01 Interactive Classe 02 Hours Live Lecture Lesson 1: Statistical Inference: Population, Samples, and Confidence Intervals • Topics Covered: Population vs. Sample, Parameters and Statistics, Standard Error, Confidence Intervals, significance level, Confidence Interval for Population Mean, Margin of Error • Objective: You will learn the concepts of population versus sample, parameters and statistics, and how to calculate and interpret standard error, confidence intervals, and understanding significance levels for making informed conclusions from sample data.
Module 06: Hypothesis testing for Decision making
02 Interactive Classes 04 Hours Live Lecture 01 Industry-Standard Project Lesson 1: Hypothesis Testing and Error Analysis in Statistical Inference Topics Covered: Null Hypothesis (H0) and Alternative Hypothesis (H1), Types of Hypothesis Tests: One-tailed and Two-tailed Tests, Type I and Type II Errors, P-value, Z-Test for Population Mean, One sample and paired T-test Objective: You will learn the fundamentals of hypothesis testing, including formulating null and alternative hypotheses, understanding one-tailed and two-tailed tests, Type I and II errors, interpreting p-values, and performing Z-tests and T-tests for population means, with real-world applications. Lesson 2: Advanced Statistical Testing and Relationship Analysis Topics Covered: Chi-Square Test for Independence, Goodness-of-Fit Test, ANOVA (Analysis of Variance), Correlation Coefficient, Likelihood Ratio Tests Objective: You will learn advanced statistical tests, including the Chi-Square Test for Independence, Goodness-of-Fit Test, and ANOVA, along with their applications in evaluating data relationships and distributions.
Module 07: Multivariate Analysis
03 Interactive Classes 06 Hours Live Lecture 01 Industry-Standard Project Lesson 1: Multivariate Analysis and Dimensionality Reduction Techniques Topics Covered: Multivariate vs. Univariate and Bivariate Analysis, Covariance vs. Correlation Matrix, Dimensionality Reduction, Multivariate Normality, Principal Component Analysis (PCA), Factor Analysis Objective: You will learn multivariate analysis techniques, contrasting univariate and bivariate approaches with multivariate methods. It covariance and correlation matrices, dimensionality reduction techniques to handle high-dimensional data for improved analysis and interpretation Lesson 3: Capstone Project 02 Topics Covered: Use of advanced statistical methods in industry level real dataset. Objective: In this project, we will apply inferential statistics and hypothesis methods along with multivariate analysis to assess prediction, segmentation and operations in the industry level data Lesson 2: Review Class Topics Covered: Review Class on both Basic and Advanced Statistics
Module 08: Introduction to R and Data Basic s
04 Interactive Classes 06 Hours Live Lecture 01 Project Lesson 1: Basic Operations and Data Structures in R Topics Covered: Overview of R and its uses in data science, setting up RStudio: interface and key features, Arithmetic and logical operations, Using built-in functions (mean, sum, length, etc.), Vectors, factors, matrices, lists, and data frames, Creating and manipulating these structures, Reading CSV and Excel files Objective: You will learn the fundamentals of R, including setting up RStudio and performing key operations in data science. This module covers data structures like vectors, matrices, and data frames, along with importing and managing external data for analysis and visualization Lesson 2: Data Manipulation with R Topics Covered: Exploring and Cleaning Data, Viewing and summarizing data (head, summary, str), Handling missing data, Data Manipulation with dplyr, Filtering rows (filter) and selecting columns (select), Sorting data (arrange), Creating new variables (mutate), Summarizing data (summarize, group_by), Combining Datasets, Joining datasets (left_join, inner_join), Binding rows and columns Objective: You will learn to summarize data, handle missing values, and use dplyr for filtering, sorting, creating variables, and summarizing data. They will also combine datasets through joins and binding, preparing data effectively for analysis. Lesson 3: Data Visualization in R Topics Covered: Introduction to ggplot2, Grammar of graphics and basic syntax, Creating basic plots (scatter plots, bar charts, line graphs), Customizing Visualizations, Adding titles, labels, and themes, Customizing colors and aesthetics, Other Visualization Tools, Using plot() for base R graphics, Exploring basic charts in R (boxplots, histograms, etc.) Objective: You will learn to create basic plots such as scatter plots, bar charts, and line graphs, and customize visualizations by adding titles, labels, themes, and adjusting colors and aesthetics. Additionally, the module explores base R visualization tools using plot() and other basic charts like boxplots and histograms, equipping students with versatile skills for effective data presentation. Lesson 4: Hands-On Project Topics Covered: Introduction to the dataset (e.g., sales, demographics, or weather data) Objective: You will learn to clean and manipulate data, perform exploratory analysis, and create visualizations to address key questions. The project aims to develop skills in data interpretation and deriving actionable insights through a step-by-step implementation approach.
Module 09: Understanding Machine Learning
03 Interactive Classes 06 Hours Live Lecture Lesson 1: Introduction to Machine Learning and Its Applications • Topics Covered: Definition of Machine Learning, Evolution of Machine Learning, Differences Between AI, Machine Learning, and Deep Learning, Applications of Machine Learning in Real-World Scenarios, Machine Learning Terminology • Objective: You will learn the fundamentals of machine learning, including its definition, historical evolution, and distinctions from artificial intelligence (AI) and deep learning. Also exploring key terminology, providing a strong foundation in the field and its practical uses. Lesson 2: Fundamentals of Machine Learning Models and Algorithms • Topics Covered: Model VS Algorithm, Training Set, Validation Set, and Test Set, Types of Machine Learning, supervised, unsupervised, and reinforcement learning, Classification vs. Regression, when to apply, which model to apply, dataset scenario • Objective: You will learn the fundamental concepts of machine learning, including the distinction between models and algorithms, and the roles of training, validation, and test sets. Also, this class covers supervised, unsupervised, and reinforcement learning, as well as the differences between classification and regression tasks. Lesson 3: Introduction to Python and Essential Libraries • Topics Covered: Python basics (variables, data types, control flow). NumPy, Pandas, Matplotlib libraries. • Objective: You will learn the foundational concepts of Python programming, including variables, data types, and control flow structures. Also, essential libraries such as NumPy for numerical computations, Pandas for data manipulation, and Matplotlib for data visualization, giving you hands-on experience in analyzing and visualizing data effectively.
Module 10: Regression Models and Feature Engineering
04 Interactive Classes 08 Hours Live Lecture 02 Industry-Standard Projects Lesson 1: Evaluation Metrics and Model Performance in Machine Learning Topics Covered: Linear Regression model, performance matrix, residuals and error, Accuracy, Precision, Recall, and F1 Score for Classification, ROC Curve and AUC, Confusion Matrix Objective: You will learn a comprehensive approach to evaluating machine learning models, focusing on linear regression performance metrics and error analysis. Additionally, you will explore residuals and error in regression models and how to interpret the Confusion Matrix for assessing classification performance. Lesson 2: Advanced Regression Techniques and Logistic Regression Topics Covered: Logistic regression, Ridge and lasso regression, parameter estimation, Ordinary Least Squares (OLS), Sigmoid Function and Binary Classification Objective: You will learn advanced regression techniques and logistic regression, including the principles of Logistic Regression and the Sigmoid Function for binary classification. Also Ridge and Lasso Regression for regularization, parameter estimation for regression analysis, helping you improve model performance and handle different types of data. Lesson 3: Feature Engineering and Handling Missing Data in Machine Learning Topics Covered: Feature Engineering Workflow, Feature Selection vs. Feature Extraction, Handling Missing Data, Types of Missing Data, Imputation (Mean, Median, Mode Imputation) Objective: You will learn feature engineering and its workflow, including the differences between feature selection and feature extraction. Also, techniques for handling missing data, types of missing data, and imputation methods like mean, median, and mode, equipping you to prepare and refine datasets to improve model performance and accuracy. Lesson 4: Data Preprocessing Techniques: Encoding, Scaling, and Anomaly Detection Topics Covered: Encoding Categorical Variables, One-Hot Encoding, Label Encoding, Frequency Encoding, Feature Scaling and Normalization, Standardization, robust scaling, data transformation, Anomaly Detection Objective: You will learn essential data preprocessing techniques, including encoding categorical variables with One-Hot, Label, and Frequency Encoding. Also, feature scaling and normalization methods like Standardization and Robust Scaling to handle outliers, ensuring data is well-prepared for machine learning models.
Module 11: Classification and Ensemble Models
03 Interactive Classes 06 Hours Live Lecture 01 Industry-Standard Project Lesson 1: Data Preprocessing and Classification Models: K-Nearest Neighbors and Decision Trees • Topics Covered: Handling Categorical and Numerical Features, Data Normalization and Standardization, Imputation of Missing Values, K-Nearest Neighbors (KNN), Distance Metrics, model evaluation, Decision Trees, Concepts of Nodes, Branches, and Leaves, Splitting Criteria: Gini Index, Entropy, and Information Gain, Pruning Techniques • Objective: You will learn essential data preprocessing techniques for classification models, including handling features, normalization, standardization, and missing value imputation. Also, model evaluation, and key concepts such as nodes, branches, leaves, splitting criteria, and pruning techniques to improve model accuracy. Lesson 2: Advanced Classification Techniques: Random Forests, SVM, and Naive Bayes • Topics Covered: Random Forests, Bagging Technique, Feature Importance and Out-of-Bag Error, Support Vector Machines (SVM), Hyperplane, Margins, and Support Vectors, Kernel Trick: Linear, Polynomial, Radial Basis Function (RBF), Naive Bayes, Conditional Probability and Bayes’ Theorem, Gaussian, Multinomial, Bernoulli • Objective: You will learn advanced classification techniques, including Random Forests and Support Vector Machines (SVM), covering concepts such as Bagging, feature importance, and out-of-bag error for Random Forests, and hyperplanes, margins, support vectors, and kernel tricks for SVM. Lesson 3: Advanced Ensemble Methods and Model Evaluation • Topics Covered: Gradient Boosting Machines (GBM), Boosting and Gradient Boosting, Residuals and Gradient Descent in GBM, XGBoost, Regularization in XGBoost, Applications, Evaluation Metrics for Classification Models, ROC Curve and AUC, Precision-Recall Curve, Confusion Matrix, Cross-Validation Techniques • Objective: You will learn advanced ensemble techniques, including Gradient Boosting Machines (GBM) and XGBoost, covering principles of boosting, gradient descent, and regularization in XGBoost. Also, model evaluation metrics such as ROC Curve, AUC, Precision-Recall Curve, Confusion Matrix, and cross-validation techniques to ensure robust model performance.
Module 12: Clustering Models
02 Interactive Classes 04 Hours Live Lecture 01 Industry-Standard Projects Lesson 1: Clustering Techniques: K-Means, Hierarchical, and DBSCAN Topics Covered: Clustering Techniques: K-Means, Hierarchical, and DBSCAN Objective: You will learn key clustering techniques in unsupervised learning, including K-Means Clustering, Hierarchical Clustering, and DBSCAN, covering their methodologies, advantages, and limitations. How to apply these methods, interpret clustering results, and choose the right technique for different datasets and problems. Lesson 2: Model Optimization and Ensemble Techniques: Cross-Validation, Hyperparameter Tuning Topics Covered: Cross-validation, hyperparameter tuning. Bias-variance trade-off, Bagging (random forest), boosting (AdaBoost, gradient boosting). Stacking Objective: You will learn advanced techniques for optimizing machine learning models, including cross-validation, hyperparameter tuning, and understanding the bias-variance trade-off. Also, ensemble methods like Bagging, Boosting, and Stacking, focusing on combining multiple models to enhance accuracy and robustness.
Module 13: Deep Learning
03 Interactive Classes 04 Hours Live Lecture 01 Industry-Standard Project Lesson 1: Foundations of Neural Networks: Perceptron, Multilayer Perceptron, Activation Functions, and Backpropagation Topics Covered: Neural networks (perceptron, multilayer perceptron). Activation functions. Backpropagation Objective: You will learn the fundamentals of neural networks, including perceptrons, multilayer perceptrons, activation functions, and backpropagation. How these elements work together to build and optimize neural network models. Lesson 2: Convolutional Neural Networks: Computer Vision Topics Covered: Convolution, pooling. Applications of CNNs (image classification, object detection) Objective: You will learn the basics of Convolutional Neural Networks (CNNs), including convolution and pooling operations, and how these techniques are used for image classification and object detection in real-world computer vision applications. Lesson 3: Sequence Modeling with Recurrent Neural Networks Topics Covered: Sequence modeling. Long short-term memory (LSTM), gated recurrent unit (GRU). Applications of RNNs (natural language processing) Objective: You will learn about sequence modeling with Recurrent Neural Networks (RNNs), focusing on Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRUs), and their applications in natural language processing (NLP) tasks like language modeling and sentiment analysis.
Module 14: Job Preparation and Freelancing guidelines
Class 1: CV, Resume and Freelancing Career •Topics Covered: Resume and Freelancing Career •Objective: You will learn practical strategies for job market entry and freelancing, including resume building, interview preparation, networking, personal branding, and client management, to effectively navigate career opportunities in Data Science
- First installment of 2100 tk is due before January 17th
- Second installment of 3500 tk is due before February 12th
- Final installment of 3200 tk is due before March 12th